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Roadmap to Fully-Automated, Pan-Antarctic, Pack-Ice Seal Surveys.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Roadmap to Fully-Automated, Pan-Antarctic, Pack-Ice Seal Surveys./
作者:
Goncalves, Bento C.
面頁冊數:
1 online resource (131 pages)
附註:
Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Contained By:
Dissertations Abstracts International84-06B.
標題:
Ecology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30243241click for full text (PQDT)
ISBN:
9798363515309
Roadmap to Fully-Automated, Pan-Antarctic, Pack-Ice Seal Surveys.
Goncalves, Bento C.
Roadmap to Fully-Automated, Pan-Antarctic, Pack-Ice Seal Surveys.
- 1 online resource (131 pages)
Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Thesis (Ph.D.)--State University of New York at Stony Brook, 2022.
Includes bibliographical references
Antarctic pack-ice seals, through their ecological role as key Antarctic krill predators, are critical to the Southern Ocean ecosystem. Shifts in sea ice distribution caused by anthropogenic climate change and krill fisheries threaten their populations. While initially surveyed by vessel or aircraft transects, very high-resolution remote sensing imagery has emerged as a safer and potentially cheaper alternative. The sheer volume of imagery, however, limits the spatial and temporal scale for human annotation of satellite imagery. AI-based, fully-automated surveys offer true scalability and, while imperfect, provide consistent annotations unaffected by observer fatigue or other factors external to the image itself. However, a pan-Antarctic survey using remote sensing comes with a number of challenges: 1) detecting seals in very-high-resolution imagery is a daunting task even for trained experts and relies heavily on contextual clues, making proper statistical treatment pivotal to go from detections to population estimates; 2) variability in lighting, terrain, off-nadir angle, and sea ice conditions impose severe limitations on the reliability of validation and test sets; and 3) limitations in our understanding of seal haul out behavior hamper our efforts to estimate the portion of seals available for detection (i.e. not submerged) at any moment in time. Here I present the recent advances in AI-powered seal detection and outline a schematic of the fully-automated pipeline that would be needed for regular pan-Antarctic seal surveying, along with requirements in terms of cost of imagery, personnel, and computational power. I will also discuss auxiliary components developed in support of an automated seal census, including sea ice segmentation models that are able to restrict input imagery to potential seal habitat only, human-level seal detection models, and the HPC middleware required to apply this efficiently at scale.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798363515309Subjects--Topical Terms:
516476
Ecology.
Subjects--Index Terms:
AntarcticaIndex Terms--Genre/Form:
542853
Electronic books.
Roadmap to Fully-Automated, Pan-Antarctic, Pack-Ice Seal Surveys.
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Antarctic pack-ice seals, through their ecological role as key Antarctic krill predators, are critical to the Southern Ocean ecosystem. Shifts in sea ice distribution caused by anthropogenic climate change and krill fisheries threaten their populations. While initially surveyed by vessel or aircraft transects, very high-resolution remote sensing imagery has emerged as a safer and potentially cheaper alternative. The sheer volume of imagery, however, limits the spatial and temporal scale for human annotation of satellite imagery. AI-based, fully-automated surveys offer true scalability and, while imperfect, provide consistent annotations unaffected by observer fatigue or other factors external to the image itself. However, a pan-Antarctic survey using remote sensing comes with a number of challenges: 1) detecting seals in very-high-resolution imagery is a daunting task even for trained experts and relies heavily on contextual clues, making proper statistical treatment pivotal to go from detections to population estimates; 2) variability in lighting, terrain, off-nadir angle, and sea ice conditions impose severe limitations on the reliability of validation and test sets; and 3) limitations in our understanding of seal haul out behavior hamper our efforts to estimate the portion of seals available for detection (i.e. not submerged) at any moment in time. Here I present the recent advances in AI-powered seal detection and outline a schematic of the fully-automated pipeline that would be needed for regular pan-Antarctic seal surveying, along with requirements in terms of cost of imagery, personnel, and computational power. I will also discuss auxiliary components developed in support of an automated seal census, including sea ice segmentation models that are able to restrict input imagery to potential seal habitat only, human-level seal detection models, and the HPC middleware required to apply this efficiently at scale.
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